From Business Problems to AI Solutions: Where Does Transformation Support Fail

TL;DR

This paper reviews approaches to translating business problems into machine learning solutions, identifying gaps in systematic guidance and proposing research directions to improve this critical step.

Contribution

It provides a structured taxonomy of existing methods, highlights the gap in systematic guidance, and proposes five research recommendations for better transformation support.

Findings

Most approaches list ML task specification as an output but lack systematic guidance.

Only four approaches provide partial guidance for deriving ML tasks.

None of the approaches offer comprehensive systematic guidance.

Abstract

Translating business problems into well-specified machine learning solutions is a prerequisite for successful AI systems, yet this upstream translation is still one of the least supported steps in existing methodologies. We conduct a structured narrative literature review of 18 approaches spanning requirements engineering (RE), machine learning (ML) project management, and automation. We organize these approaches into a taxonomy of four families and compare them across six input artifact categories, six output artifact categories, and a transformation framework of seven stages, grounded in RE refinement theory and ML lifecycle process. Our study shows that most approaches list ML task or algorithm specification among their expected outputs, yet only four provide partial guidance for deriving it, and none provides systematic guidance. We characterize this gap as the Analytics Translation Problem (ATP) and derive five research recommendations addressing multi-formulation exploration, task derivation guidance, constraint-algorithm filtering, probabilistic traceability, and data-triggered revision.